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Liczba wyników
2015 | 15 | nr 1 | 22--33
Tytuł artykułu

Geometric Method of Determining Hazard for the Continuous Survival Function

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A basic assumption in proportional intensity models is the proportionality, that each covariate has a multiplicative effect on the intensity. The proportionality assumption is a strong assumption which is not always necessarily reasonable and thus needs to be checked. The survival analysis often employs graphic methods to study hazard proportionality. In this paper a geometrical method for determining the value of the hazard function on the basis of the continuous survival function was proposed. This method can be used to compare the intensity of the event for objects belonging to two subgroups of the analysed population. If we have graphs of survival function, then an analysis of the tangents at a specific time and their roots enables us to find the intensity and to study the relationship between them for different subgroups. This method can also be useful when studying the proportionality of hazard. It is a condition for the use of the Cox proportional hazards model. The above method was used to evaluate the effect of unemployment benefit and gender on unemployment and on the intensity of finding a job.(original abstract)
Rocznik
Tom
15
Numer
Strony
22--33
Opis fizyczny
Twórcy
  • University of Szczecin, Poland
Bibliografia
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  • Huzurbazar A.V., (2005), Flowgraph Models for Multistate Time-to-Event-Data, New Jersey: Wiley.
  • Hosmer D.W., Lemeshow S., (1999), Applied Survival Analysis. Regression Modeling of Time to Event Data, New York: Wiley.
  • Kalbfleisch J.D., Prentice R.L., (2002), The statistical analysis of failure time data, 2nd Edition, New York: Wiley.
  • Klein J.P., Moeschberger M.L., (2003), Survival Analysis. Techniques for Censored and Truncated Data, 2nd Edition, New York: Springer.
  • Kleinbaum D.G., Klein M., (2005), Survival Analysis, 2nd Edition, New York: Springer.
  • Kraus D., (2007), Data-driven smooth tests of the proportional hazards assumption, Lifetime Data Analysis, 13: 1-16. [Web of Science]
  • Kvaløy J.T., Neef L.R., (2004), Tests for the Proportional Intensity Assumption Based on the Score Process, Lifetime Data Analysis, 10: 139-157. [CrossRef]
  • Lin D.Y., Wei L.J., Ying Z., (1993), Checking the Cox model with cumulative sums of the martingale-based residuals, Biometrika, 80: 557-572. [CrossRef]
  • Machin D., Cheung Y.B., Parmar M.K., (2006), Survival Analysis. A practical Approach, 2nd Edition, Chichester: Wiley.
  • O'Quigley J., Pessione F., (1989), Score tests for homogeneity effects in the proportional hazards model, Biometrics, 45: 135-144. [CrossRef]
  • O'Quigley J., (2008), Proportional Hazard Regression, New York: Springer.
  • Scheike T., Martinussen T., (2004), On estimation and tests of time-varying effects in the proportional hazards model, Scandinavian Journal of Statistics, 31: 51-62.
  • Schoenfeld D., (1982), Partial residuals for the proportional hazards regression model, Biometrika, 69: 239-241. [CrossRef]
  • Selvin S. (2008)., Survival Analysis for Epidemiologic and Medical Research. A Practical Guide, Cambridge: Cambridge University Press.
  • Wei L.J., (1984), Testing goodness of fit for the proportional hazards model with censored observations, Journal of the American Statistical Association, 79: 649-652. [CrossRef]
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171406265

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